code: def __init__(self):
      super().__init__()
      self.conv1 = torch.nn.Conv2d(
          in_channels=3, out_channels=64, kernel_size=3
      )
      self.relu1 = torch.nn.ReLU()
      self.conv2 = torch.nn.Conv2d(
          in_channels=64, out_channels=32, kernel_size=3
      )
      self.relu2 = torch.nn.ReLU()
      self.fcn = torch.nn.Linear(32, 2)
  
imports: import os
  from pathlib import Path
  from typing import Any, Callable, Optional, Tuple
  import tensorflow
  import tensorflow.keras
  import torch
  from tensorflow.keras import Model, layers
  from transformers import AlbertModel, AlbertTokenizer
  from nebullvm.config import TRAIN_TEST_SPLIT_RATIO, CONSTRAINED_METRIC_DROP_THS
  from nebullvm.core.models import DeepLearningFramework, ModelParams, DataType, DeviceType, Device, QuantizationType
  from nebullvm.operations.conversions.huggingface import convert_hf_model
  from nebullvm.operations.conversions.pytorch import convert_torch_to_onnx
  from nebullvm.operations.measures.measures import LatencyOriginalModelMeasure, MetricDropMeasure
  from nebullvm.operations.measures.utils import compute_relative_difference
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.transformations import MultiStageTransformation
  from nebullvm.tools.utils import gpu_is_available, extract_info_from_data
label_desc: Use image node_id 1 to create a new TestModel object from inherited base classes: torch.nn with example: obj = TestModel()